Predicting structured objects with support vector machines

Authors:
Thorsten Joachims;Thomas Hofmann;Yisong Yue;Chun-Nam Yu
Affiliations:
Cornell University, Ithaca, NY;Google Inc., Zürich, Switzerland;Cornell University, Ithaca, NY;Cornell University, Ithaca, NY
Venue:
Communications of the ACM - Scratch Programming for All
Year:
2009

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Abstract

Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores a generalization of Support Vector Machines (SVMs) for such complex prediction problems.